Factors that influence small animal veterinarians’ opinions and actions regarding cost of care and effects of economic limitations on patient care and outcome and professional career satisfaction and burnout
Bibliographic record
Abstract
OBJECTIVE To determine small animal veterinarians' opinions and actions regarding costs of care, obstacles to client education about veterinary care costs, and effects of economic limitations on patient care and outcome and professional career satisfaction and burnout. DESIGN Cross-sectional survey. SAMPLE 1,122 small animal practitioners in the United States and Canada. PROCEDURES An online survey was sent to 37,036 veterinarians. Respondents provided information regarding perceived effects of client awareness of costs and pet health insurance coverage on various aspects of practice, the influence of client economic limitations on professional satisfaction and burnout, and proposals for addressing those effects. RESULTS The majority (620/1,088 [57%]) of respondents indicated that client economic limitations affected their ability to provide the desired care for their patients on a daily basis. Approximately half (527/1,071 [49%]) of respondents reported a moderate-to-substantial level of burnout, and many cited client economic limitations as an important contributing factor to burnout. Only 31% and 23% of respondents routinely discussed veterinary costs and pet insurance, respectively, with clients before pets became ill, and lack of time was cited as a reason for forgoing those discussions. Most respondents felt improved client awareness of veterinary costs and pet health insurance would positively affect patient care and client and veterinarian satisfaction. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested most small animal practitioners believe the veterinary profession needs to take action at educational and organizational levels to inform pet owners and educate and train veterinary students and veterinarians about the costs of veterinary care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".